End of life care for cancer patients is a major challenge for health care providers. As society ages, increasing numbers of Americans will contract and eventually die of cancer. Diagnosis of incurable cancer at different stages of life span holds different meaning for patient, providers and society. Efforts to advance the knowledge of providers in end of life care must be based on accurate data on middle-aged and older patients, current practices, and outcomes for patients, families and health care systems. Of special concern are patients who are hospitalized in the late stages of their disease. Studies suggest that patient's age may be an important factor in health communication and decision making near end of life and in practices and outcomes, both in-hospital and post-discharge. The SUPPORT dataset offers a unique opportunity to examine age differences in communication and preferences relative to practices and outcomes for advanced cancer patients during hospitalization and post discharge. SUPPORT, a five year multi- site study identified hospitalized, seriously ill patients with one or more of nine conditions (3 were cancer) and followed them for six months. 1,424 cancer patients completed multiple interviews (in hospital) and 920 completed a month 2 interview (post discharge). Investigators will examine age group differences in end of life care practices and outcomes for cancer patients in SUPPORT. We will test hypotheses about differences in care practices (i.e., aggressiveness of care and intensity of resource use) and outcomes (i.e., quality of life and satisfaction with care) among older adults, middle aged and young adult patients. We will conduct analyses and test hypotheses about patient's and their physician's preferences and communication about aggressive care. Discrepancy between patient and physician preferences will be examined in relation to age. These variables will be tested as predictors of practices and outcomes for the three age groups during hospitalization and post discharge. Patient, physician and care system variables will be controlled. The primary analyses will use general linear model methods to examine the research questions and hypotheses. The focus will be on (1) full model specification and examination of interactions among factors, including examination of homogeneity of regressions to understand interactions between classification effects and covariates; (2) repeated measures models to study change over time and to look at congruence of patient, surrogate and physician perspectives; and (3) mixed models to take into account the variance associated with physicians' and patients. Findings will contribute to policy guidelines and education regarding end of life care for cancer patients at different stages of the adult life span.